Landslides and tsunami generation in large-scale flume experiments and numerical particle-following simulations
Bibliographic record
Abstract
<p>Tsunamis generated by highly mobile slides in large-scale flume experiments are simulated with a numerical model called the Particle Finite Element Method (PFEM). The numerical technique combines a Lagrangian finite element solution with an efficient remeshing algorithm, and is capable of accurately tracking the evolving fluid free-surface and velocity distribution in highly unsteady flows. The slide material is water, which represents an avalanche or debris flow with high mobility, and the reservoir depth is varied, thereby achieving a range of different near-field wave conditions from breaking waves to near-solitary waves. Experimental observations of fluid velocity and water surface levels are obtained using high-speed digital cameras, acoustic sensors and capacitance wave probes, and the data are used to analyze the accuracy of the PFEM predictions. The numerical model shows the capability of holistically reproducing the entire problem from landslide motion, to impact with water, to wave generation and propagation. Very good agreement with the experimental observations are obtained, in terms of landslide velocity and thickness, wave time series, maximum wave amplitude, wave speed and wave shape. The results demonstrate the potential of this numerical method for simulating mass flows, impacts with water, and the tsunamis generation process.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".